Gmail

Résumé

Gmail (abréviation de Google Mail) (couramment prononcé en français européen, en anglais américain ) est un service de messagerie électronique, gratuit pour les particuliers, proposé par Google. Les messages reçus sur un compte Gmail peuvent être lus via un client de messagerie, une application mobile ou avec un navigateur web. Certaines fonctionnalités du service ne sont cependant accessibles qu’à travers le navigateur web. L'adresse Gmail sert généralement d’identifiant pour l'authentification à de nombreux services proposés par Google (YouTube, Blogger, Android , google play store ), justifiant son slogan Tout Google avec un seul compte. Les utilisateurs de Gmail ne l'utilisent donc pas nécessairement pour échanger des courriers électroniques, mais simplement pour accéder à leur compte Google. À son lancement le , l’inscription nécessitait une invitation. Deux ans plus tard, la version bêta est ouverte au public. À l’époque, la capacité initiale était de , et a augmenté régulière

Source officielle

https://fr.wikipedia.org/wiki/Gmail

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Concepts associés (53)

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Cours associés (14)

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Séances de cours associées (20)

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Artificial immune systems (AIS) use the concepts and algorithms inspired by the theory of how the human immune system works. This document presents the design and initial evaluation of a new artificial immune system for collaborative spam filtering. Collaborative spam filtering allows for the detection of not-previously-seen spam content, by exploiting its bulkiness. Our system uses two novel and possibly advantageous techniques for collaborative spam filtering. The first novelty is local processing of the signatures created from the emails prior to deciding whether and which of the generated signatures will be exchanged with other collaborating antispam systems. This processing exploits both the email-content profiles of the users and implicit or explicit feedback from the users, and it uses customized AIS algorithms. The idea is to enable only good quality and effective information to be exchanged among collaborating antispam systems. The second novelty is the representation of the email content, based on a sampling of text strings of a predefined length and at random positions within the emails, and a use of a custom similarity hashing of these strings. Compared to the existing signature generation methods, the proposed sampling and hashing are aimed at achieving a better resistance to spam obfuscation (especially text additions) - which means better detection of spam, and a better precision in learning spam patterns and distinguishing them well from normal text - which means lowering the false detection of good emails. Initial evaluation of the system shows that it achieves promising detection results under modest collaboration, and that it is rather resistant under the tested obfuscation. In order to confirm our understanding of why the system performed well under this initial evaluation, an additional factorial analysis should be done. Also, evaluation under more sophisticated spammer models is necessary for a more complete assessment of the system abilities.

2007

Chargement

Artificial Immune System For Collaborative Spam Filtering

Jean-Yves Le Boudec, Slavisa Sarafijanovic

Springer Verlag2008

Chargement

Improving Digest-Based Collaborative Spam Detection

Jean-Yves Le Boudec, Slavisa Sarafijanovic

Spam is usually sent in bulk. A bulk mailing consists of many copies of the same original spam message, each sent to a different recipient. The copies are usually obfuscated, i.e. modified a bit in order to look different from each other. In collaborative spam filtering it is important to determine which emails belong to the same bulk. This allows, after observing an initial portion of a bulk, for the bulkiness scores to be assigned to the remaining emails from the same bulk. This also allows the individual evidence of spamminess to be joined, if such evidence is generated by collaborating filters or users for some of the emails from an initial portion of the bulk. Then, the observed bulkiness and the estimated spamminess of a bulk can be used to better filter the remaining emails from the same bulk. The work by Damiani et al. [2] ("open-digest paper") is well know and often cited for its positive findings about the properties of a digest-based collaborative spam detection technique. The technique produces similar digests out of similar emails, and uses them to find out which emails belong to the same bulk. Based on the experimental evaluation, the paper suggests that the technique provides bulk-spam detection that is robust to increased obfuscation efforts by spammers, and low miss-detection of good emails. We first repeat and extend some of the open-digest paper [2] experiments, using the simplest spammer model from that paper. We find that the conclusions of the open-digest paper are rather miss- leading. Then we propose and evaluate, under the same spammer model, a modified version of the original digest technique. The modified version greatly improves the resistance of spam detection against increased obfuscation effort by spammers, while keeping miss-detection of good emails at a similar level. Based on the observed results, we discuss possible additional modifications and algorithms that could be added on top of the modified digest technique to further improve its filtering performance.

2008

Chargement

Artificial Immune System For Collaborative Spam Filtering

Jean-Yves Le Boudec, Slavisa Sarafijanovic

Springer Verlag2008

2007

Improving Digest-Based Collaborative Spam Detection

Jean-Yves Le Boudec, Slavisa Sarafijanovic

2008